An automated system of soil sensor-based site-specific seeding for silage maize: A proof of concept

Abstract

Despite recent studies of map-based site-specific seeding (SSS) revealing improved agronomic and economic outcomes over uniform rate seeding (URS), to our best knowledge no visible and near infrared spectroscopy (vis-NIRS) sensor-based SSS studies exist to date. This study aimed to develop and evaluate an automated sensor-based SSS technology for silage maize (Zea mays L.) production. An on-line visible and near-infrared reflectance spectroscopy (vis-NIRS) sensor was installed in front of a tractor to provide real-time input data to control the seed rate using a precision seeding machine mounted at the back of the tractor. A LabVIEW-based software was developed and used to predict soil fertility index using on-line vis-NIR spectra, which was then used to calculate the seed rate and transfer it to the controller of the seeding machine. The agronomic and economic benefits of SSS were compared with URS under a one-site-year experiment. Results showed that the proposed sensor-based SSS technology was 87.5% efficient in controlling the desired seed rates, according to the soil fertility status. In parallel with the observed spatial similarity between predicted soil fertility index and actual seed rates, a strong linear association (R2 = 0.80) was also achieved. As a result, SSS improved silage yield by 1.4 t ha−1 (4.4%), while sowing a lower seed rate (86.4 kSeeds ha−1) than the URS (90 kSeeds ha−1). This improved gross margin by 91 € ha−1, of which only 7 € ha−1 was attributed to savings on seed cost. The proposed sensor-based SSS technology was technically sound and thus transforms within-field fertility variations into agro-economic benefits effectively.